Locating and/or classifying objects based on radar data, with improved reliability at different distances

ABSTRACT

A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.

CROSS REFERENCE

The present application claims the benefit under 35 U.S.C. § 119 ofGerman Patent Application No. DE 102018222800.0 filed on Dec. 21, 2018,which is expressly incorporated herein by reference in its entirety.

FIELD

The present invention relates to locating and/or classifying objectssituated in an area that is monitored by a radar sensor.

BACKGROUND INFORMATION

In order for a vehicle to be able to move in road traffic in an at leastsemi-automated manner, it is necessary to detect the surroundings of thevehicle, and to initiate countermeasures if there is a risk of acollision with an object in the surroundings of the vehicle. Creating arepresentation of the surroundings and localization are also necessaryfor safe automated driving.

The detection of objects with the aid of radar is independent of thelight conditions, and for example even at night is possible at a fairlygreat distance without oncoming traffic being blinded by high-beamlight. In addition, the distance and speed of objects result directlyfrom the radar data. This information is important for assessing whethera collision with the objects is possible. However, the type of object inquestion is not directly recognizable from radar signals. Thisrecognition is presently achieved by computing attributes from thedigital signal processing.

U.S. Pat. No. 8,682,821 B2 describes the classification of radarsignals, with the aid of machine learning, as to whether they originatefrom the movement of certain objects or nonhuman animals. This knowledgemay be utilized to avoid false alarms when monitoring an area for humanintruders, or also to select the correct action to avoid a collision forat least semi-automated driving.

SUMMARY

In accordance with the present invention, an example method for locatingand/or classifying at least one object in an area that is monitored byat least one radar sensor is provided. The radar sensor includes atleast one transmitter and at least one receiver for radar waves.

The signal recorded by the receiver is converted into a two- ormultidimensional frequency representation. In this frequencyrepresentation, in particular for example one direction may representthe distance of an object from the radar sensor, and another directionmay represent azimuth angle α of the object relative to the radarsensor. In addition, for example a further direction may represent anelevation angle of the object relative to the radar sensor and/or aspeed of the object relative to the radar sensor.

At least a portion of the two- or multidimensional frequencyrepresentation is supplied as an input to an artificial neural network(ANN), which includes a sequence of layers with neurons. The locatingand/or the classification of the object are/is taken from the ANN as anoutput.

In addition, a piece of dimensioning information which characterizes thesize and/or absolute position of objects detected in the portion of thetwo- or multidimensional frequency representation is supplied to atleast one layer of the ANN.

It has been recognized that the piece of information concerning objects,contained in the portion of the two- or multidimensional frequencyrepresentation, is frequently ambiguous, in particular with respect toits classification. Thus, for example, a near, small object on the onehand and a distant, large object on the other hand may have a verysimilar appearance in the two- or multidimensional frequencyrepresentation. This is due to the physical nature of the radarmeasurement, which as a measured variable ultimately detects whichportion of the radiation that is emitted into the monitored area isreflected, and from what solid angle in this area.

Such ambiguities may be resolved with the additional piece ofdimensioning information. For example, the classification by the ANN mayfrom the outset concentrate on objects in a certain size range that isconsistent with the piece of dimensioning information. However, it isalso possible, for example, to initially form an ambiguousclassification, and to subsequently resolve this ambiguity, using thepiece of dimensioning information.

Even a piece of very rough dimensioning information achieves anoticeable effect. Thus, for example, just the piece of information ofwhether the analyzed portion of the two- or multidimensional frequencyrepresentation refers to an object 10 m away or an object 100 m awayexcludes many incorrect classifications. The more detailed the piece ofdimensioning information, the more incorrect classifications that may beexcluded, and the greater the likelihood that the ANN ultimately outputsa correct classification.

Furthermore, any additionally available piece of dimensioninginformation also improves the locating of objects. Thus, for example,inaccuracies resulting from radar waves also reflecting on undesirablelocations, or various objects having different transparencies to radarwaves, may be suppressed.

In one particularly advantageous embodiment, the piece of dimensioninginformation characterizes

-   -   a distance d of at least one location, detected by the portion        of the two- or multidimensional frequency representation, from        the radar sensor, and/or    -   an azimuth angle α at which at least one location, detected by        the portion of the two- or multidimensional frequency        representation, is situated relative to the radar sensor, and/or    -   a distance d′ of multiple locations, detected by the portion of        the two- or multidimensional frequency representation, from one        another.

As discussed above, based on distance d, a distinction may be made, forexample, between a near, small object on the one hand and a distant,larger object on the other hand.

For the reflection of radar waves, in the geometrical optics model thestandard law of reflection applies, according to which a beam striking asurface at a certain incidence angle leaves this surface at acorresponding reflection angle. At the same time, in most radar sensorsthe transmitter and the receiver are comparatively close to one another.Frontal radiation from the transmitter that is incident on a surfacetherefore reaches the receiver with a greater likelihood and intensitythan radiation that is incident on the same surface at a very acuteangle. The influence of this effect on the location and/orclassification ultimately obtained may be reduced by considering a pieceof dimensioning information with respect to azimuth angle α.

Distance d′ of multiple locations, detected by the portion of the two-or multidimensional frequency representation, from one another may bedirectly utilized as a measure for spatial dimensions of objects.

In one particularly advantageous embodiment, the piece of dimensioninginformation is supplied to the at least one layer of the ANN as afurther input variable that is independent of the content of the portionof the two- or multidimensional frequency representation. For example,the piece of dimensioning information may be supplied to the layer inthe form of one or multiple scalar value(s). Thus, for example, thelayer may include input variables that are derived solely from theportion of the two- or multidimensional frequency representation that isinput into the ANN, and the piece of dimensioning information may beadditionally associated with these input variables.

When the portion of the two- or multidimensional frequencyrepresentation passes through the sequence of the layers in the ANN, itsdimensionality is generally gradually reduced by one or multiple poolinglayers. In this way, the ANN is able to overcome a large dimensionalitydifferential between the input and the ultimately obtainedclassification. Thus, for example, a section of the frequencyrepresentation of 150×150 pixels has a dimensionality of 22,500, whilethe dimensionality of the classification corresponds to the number ofvarious objects to be recognized and is usually less than 1,000. This inturn means that the further the processing has already advanced in theANN, the greater the influence of a piece of dimensioning informationthat is supplied in the form of one or multiple independent inputvariables. The selection of the layer or layers into which theindependent input variable is supplied is thus a fine tuning for theinfluence of this additional input variable on the classificationresult. The influence is greatest when the piece of dimensioninginformation is not added until approximately the end of a classificationthat is already largely completed. Thus, the piece of dimensioninginformation may decide approximately at the end whether a compact carthat is near or a semitrailer that is more distant is recognized.

In another particularly advantageous embodiment, the piece ofdimensioning information is added to the portion of the two- ormultidimensional frequency representation as an additional informationlayer, and/or this piece of dimensioning information is superimposed onthe input of the at least one layer of the ANN. This type ofconsideration is particularly suited for a piece of dimensioninginformation that varies over the frequency representation. For example,if a direction of the frequency representation represents distance dfrom the radar sensor, the accuracy is greatly improved if this is alsoreflected in the piece of dimensioning information. In addition, fewer,or even no, changes to the architecture of the ANN are necessary inorder to take the piece of dimensioning information into account. Forexample, the piece of dimensioning information may be added to a portionof the frequency representation, present as a two- or multidimensionalimage, as an additional color channel.

In one advantageous embodiment, the piece of dimensioning information istaken from the two- or multidimensional frequency representation itself.For example, if one direction of this frequency representationrepresents distance d from radar sensor and another direction representsazimuth angle α relative to the radar sensor, the complete piece ofdimensioning information is initially present. However, if a portion isnow selected from this frequency representation (region of interest,ROI) in order to choose a certain object for the classification, thisportion initially does not contain the piece of information concerningfrom which location in the original frequency representation it has beentaken. Thus, this portion is lacking the originally present piece ofdimensioning information. As the result of this piece of dimensioninginformation now being transferred to the selected portion, the act ofselecting this portion no longer creates ambiguities in theclassification.

In another particularly advantageous embodiment, the piece ofdimensioning information is taken from measuring data that have beendetected via a further sensor that is different from the radar sensor.For example, distance d or azimuth angle α may be measured via LIDAR ora similar technique. Alternatively or also in combination, the piece ofdimensioning information may be taken from a digital map, for example.Such a digital map may contain, for example, stationary objects such asbuildings or traffic infrastructure. A vehicle that is able to determineits own position in an arbitrary manner (by satellite, for example) may,based on this position, take information concerning its surroundingsfrom the digital map.

In another particularly advantageous embodiment, in the portion of thetwo- or multidimensional frequency representation, the informationoutside a two- or multidimensional sphere is suppressed around thecenter. The selection of this portion may be even further refined inthis way. In many cases, the portion of the frequency representationactually of interest with regard to a certain object has the shape of acircle or a sphere. However, a portion having this shape generallycannot be directly input into the ANN, and instead the ANN expects aninput in the form of a rectangle or a square. When the observed settingcontains multiple objects that are close together, an edge area of therectangle or square with which a first object is to be selected mayalready contain signal components that originate from a neighboringobject. These undesirable signal components may be suppressed by thedescribed circle- or sphere-shaped masking. The masking may take place,for example, with a sharp cutting edge, so that all values of thefrequency representation that are outside the circle or the sphere areset to zero. However, it is also possible, for example, to attenuate allsignal components that are outside the circle or the sphere, using alinear, quadratic, exponential, or any other arbitrary function of thedistance from the center. This “softens” the cutting edge and avoidsedge artifacts.

In addition, it has been recognized that the accuracy of theclassification may be increased not only by taking into accountadditional spatial piece of dimensioning information, but also by takinginto account additional temporal information.

For example, if a certain object has been recognized in the monitoredarea at a first point in time, this object should continue to berecognized as long as it is actually situated in the monitored area. Anobject that suddenly appears and then suddenly disappears, for example,is possibly not actually present at all. Rather, this may involve“ghosting” or some other artifact.

Likewise, based on certain movement patterns of an object, for example,it may be deduced that the object can or cannot belong to a certainclass. In the mentioned example, in which a compact car that is near isto be distinguished from a semitrailer that is farther away, for examplea turning circle that the semitrailer is not able to negotiate mayindicate that the object cannot be the semitrailer.

Therefore, the present invention further relates to an additional methodfor locating and/or classifying at least one object in an area that ismonitored by at least one radar sensor. The radar sensor includes atleast one transmitter and at least one receiver for radar waves.

The signal recorded by the receiver at various points in time isconverted in each case into a two- or multidimensional frequencyrepresentation. Analogously to the method described above, in thisfrequency representation, in particular for example one direction mayrepresent the distance of an object from the radar sensor, and anotherdirection may represent azimuth angle α of the object relative to theradar sensor. In addition, for example a further direction may representan elevation angle of the object relative to the radar sensor and/or aspeed of the object relative to the radar sensor.

At least a portion of each of these two- or multidimensional frequencyrepresentations is supplied as an input to an artificial neural network(ANN), which includes a sequence of layers with neurons. The locatingand/or the classification of the object are/is taken from the ANN as anoutput.

In one particularly advantageous embodiment of the present invention, arecurrent ANN is selected in which the output of at least one neuron issupplied as an input to at least one neuron of the same layer, or to atleast one neuron of a preceding layer in the sequence. These networksare particularly suited for classifying temporal sequences such asmovement patterns. For example, the movement patterns may be used todistinguish various types of vehicles from one another, such as on theone hand bicycles that are operated only with muscle power, and on theother hand motorcycles, which have a very similar silhouette.

In another particularly advantageous embodiment of the presentinvention, multiple portions of the two- or multidimensional frequencyrepresentations are jointly supplied to the same ANN as inputs. Even ifit is not a recurrent network, the ANN is thus enabled to learn movementpatterns.

In another particularly advantageous embodiment, multiple portions ofthe two- or multidimensional frequency representations are supplied insuccession to the same ANN as inputs. The locatings and/orclassifications obtained in each case from the ANN are aggregated toform a locating 31 and/or a classification 32.

For example, the locating may be made more accurate by averagingmultiple locatings of an object which either does not move at all, ordoes not move in a known manner, relative to the vehicle.

Even better, multiple classifications that are obtained at differentpoints in time may be aggregated for the same object. In manyapplications, in particular in road traffic, the objects in question maysuddenly change their behavior, but cannot become objects of acompletely different type. If the object is classified differently atdifferent points in time, it is thus likely that some of theseclassifications are not correct, and the ANN has, for example, been“deceived” by poor signal quality or by artifacts.

In many cases, averaging classifications makes no sense, since among theclasses there is no well-defined scalar order that satisfies theparticular application. Thus, an arithmetic mean does not exist fortraffic signs or vehicles. Therefore, in another particularlyadvantageous embodiment the classifications are aggregated into oneclassification. Thus, for example, if a stop sign has been recognizedfive times, a speed limit sign two times, and a no parking sign onetime, the stop sign is the classification that is ultimately output.

According to the above discussion, in one particularly advantageousembodiment a radar sensor that is mounted on a vehicle is selected. Inparticular, a vehicle may be controlled as a function of the ascertainedlocating and/or of the ascertained classification of at least oneobject. For example, an actuating signal may be provided for at leastone actuator of a vehicle as a function of the ascertained locatingand/or of the ascertained classification, and the actuator may becontrolled with this actuating signal.

The described methods may be enhanced, for example, by checking whetherthe up-to-date planned and/or traveled trajectory of a vehiclecontact(s) the location and/or the trajectory of a recognized object. Ifthis is the case, there may be a risk of a collision. A warning devicemay be activated in this case. Alternatively or also in combination, adrive system, a braking system, and/or a steering system of the vehiclemay be controlled to prevent the contact.

The methods described above may be completely or partially implementedin software. This software provides the direct customer benefit ofimproving the reliability of the locating and classification of objectsbased on radar measurements, in particular when the objects appear atvery different distances in front of the radar sensor. This software maybe distributed, for example, as an update or upgrade to an existingsystem that is used for the locating and/or classification, and in thisrespect is a stand-alone product. Therefore, the present inventionfurther relates to a computer program containing machine-readableinstructions which, when executed on a computer and/or on a controlunit, prompt the computer and/or the control unit to carry out one ofthe described methods. Moreover, the present invention relates to amachine-readable data medium or a download product containing thecomputer program.

Furthermore, the present invention relates to a computer and/or acontrol unit and/or a device that include(s) the computer program, themachine-readable data medium, and/or the download product. Alternativelyor also in combination, for this purpose the computer, the control unit,and/or the device may be designed in any other arbitrary mannerspecifically to carry out one of the described methods, for exampleincorporating the functionality of the method into application-specificintegrated circuits (ASICs) or into a field programmable gate array(FPGA).

Further measures that enhance the present invention are explained ingreater detail below, together with the description of the preferredexemplary embodiments of the present invention, with reference tofigures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows one exemplary embodiment of method 100.

FIG. 2a-2c show examples of options for introducing a piece ofdimensioning information 16.

FIGS. 3a and 3b show examples of options for suppressing interferingsignal components 19.

FIG. 4 shows one exemplary embodiment of method 200.

FIG. 5 shows examples of scenarios in which the accuracy ofclassification 32 may be increased.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 shows one exemplary embodiment of method 100. Signal 14 recordedby receiver 12 is converted into a two- or multidimensional frequencyrepresentation 15 in step 110. This frequency representation 15 may besubsequently further processed in its entirety. However, as depicted atthe lower left in FIG. 1, it is also possible to select a portion 15′which, for example, contains only the signal components that originatefrom one object.

Frequency representation 15 or portion 15′ thereof is supplied to ANN 4in step 120. Locating 31 and/or classification 32 of at least one object3 are/is taken from ANN 4 as an output in step 130.

Piece of dimensioning information 16, which may be obtained in variousways, is supplied to at least one layer 4 a through 4 c of ANN 4 in step125. FIG. 1 illustrates several examples of options for obtaining andintroducing piece of dimensioning information 16.

Piece of dimensioning information 16 may be supplied to the at least onelayer 4 a through 4 c of ANN 4 as an additional input variable accordingto block 126. Piece of dimensioning information 16 may be added toportion 15′ of frequency representation 15, which may also correspond tocomplete frequency representation 15, as an additional information layeraccording to block 127 a. Piece of dimensioning information 16 may besuperimposed on the input of the at least one layer 4 a through 4 c ofANN 4 according to block 127 b. This input, for example in first layer 4a of ANN 4, may include portion 15′ of frequency representation 15, andprocessing products thereof in deeper layers 4 b, 4 c.

Piece of dimensioning information 16 may be taken from two- ormultidimensional frequency representation 15 according to block 128. Asdepicted at the lower left in FIG. 1, frequency representation 15 isdimensioned in one direction with distance d from radar sensor 1, and inthe other direction with azimuth angle α relative to the radar sensor.As explained in greater detail with reference to FIG. 2b , distance dfrom the location from which the signal component originates may beassociated with each signal component in portion 15′ of frequencyrepresentation 15.

In addition, piece of dimensioning information 16 may be taken frommeasuring data 5 a that have been detected by a further sensor 5 that isdifferent from radar sensor 1, according to block 129. Piece ofdimensioning information 16 may also be taken from a digital map 6.

In the example shown in FIG. 1, radar sensor 1 is mounted on a vehicle50. Following the recognition of an object 3, a check is made in step140 as to whether up-to-date planned and/or traveled trajectory 50 a ofvehicle 50 contacts location 3 b and/or trajectory 3 a of object 3. Ifthis is the case (truth value 1), countermeasures may be taken toprevent this contact. FIG. 5 illustrates an example of a trafficsituation in which this is advantageous.

In particular, a warning device 51 a that is perceivable to the driverof vehicle 50 and/or a signal horn 51 b that is perceivable outsidevehicle 50 may be activated in step 150. Alternatively or also incombination, a drive system 52, a braking system 53, and/or a steeringsystem 54 of vehicle 50 may be controlled in step 160 in order toprevent the contact.

In general, vehicle 50 is controlled according to block 148 as afunction of ascertained locating 31 and/or of ascertained classification32 of at least one object 3. In particular, an actuating signal 149 a,149 b for at least one actuator 51 a, 51 b, 52-54 of vehicle 50 isprovided in subblock 149 as a function of locating 31 and/or ofclassification 32, and this actuator 51 a, 51 b, 52 through 54 iscontrolled with this actuating signal 149 a, 149 b in steps 150 and/or160.

FIG. 2a schematically shows how a portion 15′ of frequencyrepresentation 15 gradually loses dimensionality while passing throughANN 4, until locating 31 and/or classification 32 of object 3 are/isultimately formed in last layer 4 c. Piece of dimensioning information16 may now be supplied, for example, to each of these layers 4 a through4 c, in addition to the input that this layer 4 a through 4 c alreadycontains anyway, as one or multiple additional input variables. This maybe one or multiple scalar values, for example. The weighting that thesevalues receive during the further processing depends, among otherthings, on how large their dimensionality is in comparison to the otherinputs of particular layer 4 a through 4 c. Since portion 15′ graduallyloses dimensionality, the weighting of piece of dimensioning information16 tends to become increasingly greater in deeper layers 4 a through 4c.

FIG. 2b schematically shows how a distance d from radar sensor 1, whichvaries within portion 15′ of frequency representation 15, may be encodedas piece of dimensioning information 16 and added to portion 15′ offrequency representation 15 as an additional information layer. In thisexample, portion 15′ and piece of dimensioning information 16 eachcontain 5×5 pixels. Consistent with FIG. 1, where the vertical axis offrequency representation 15 is dimensioned with distance d, distance dalso increases along the vertical axis of portion 15′ of frequencyrepresentation 15. Each pixel of piece of dimensioning information 16associates a distance d with the corresponding pixel of portion 15′ offrequency representation 15.

FIG. 2c schematically shows how distance d′ from the center of portion15′, which varies within portion 15′ of frequency representation 15, maybe encoded, similarly as for piece of dimensioning information 16 inFIG. 2b , and added to portion 15′ of frequency representation 15 as anadditional information layer.

FIGS. 3a and 3b schematically show two examples of options of howinterfering signal components 19 may be suppressed when evaluatingportion 15′ of frequency representation 15.

A distinct circle 18 is drawn around center 17 of portion 15′ in FIG. 3a. The signal components within this circle 18 are maintained for thefurther evaluation of portion 15′, while all signal components outsidethis circle are set to zero. Interfering signal components 19 depictedby way of example thus remain without affecting locating 31 orclassification 32 that is ultimately generated from portion 15′.

Analogously, in the example shown in FIG. 3b , circle 18 has a “soft”edge. This means that the attenuation of the signal componentscontinuously increases radially outwardly along this soft edge. Here aswell, interfering signal components 19 remain without affecting locating31 or classification 32.

FIG. 4 shows one exemplary embodiment of method 200. Analogously to step110, signal 14 recorded by receiver 12 is transformed into a two- ormultidimensional frequency space in step 210. However, in contrast toFIG. 1 this now takes place at multiple points in time, so that multipletwo- or multidimensional frequency representations 15 a through 15 cresult, from which in turn portions 15 a′ through 15 c′ may be selectedin each case.

These portions 15 a′ through 15 c′, which once again may each becomplete frequency representation 15 a through 15 c, are supplied to ANN4 in step 220. For this purpose, three examples of options are depictedin FIG. 4. Locating 31 and/or classification 32 of at least one object 3are/is formed in step 230.

According to block 221, a recurrent ANN 4 may be used in which there areconnections within layers 4 a through 4 c, and/or back-references fromdeeper layers 4 a through 4 c to higher layers 4 a through 4 c.

Portions 15 a′-through 5 c′ of frequency representations 15 a-15 c maybe combined into a single input, which is then supplied to ANN 4,according to block 222.

Each portion 15 a′ through 15 c′ of a frequency representation 15 athrough 15 c may be separately supplied to ANN 4 according to block 223,in each case resulting in a separate locating 31 a through 31 c and/or aseparate classification 32 a through 32 c. Locatings 31 a through 31 cand/or classifications 32 a through 32 c are then aggregated accordingto block 231, it being optionally possible for a majority vote to beformed from classifications 32 a through 32 c according to subblock 231a.

Further steps 240-260, which make use of overall generated locating 31and/or overall generated classification 32, run completely analogouslyto steps 140-160 described in greater detail in conjunction with FIG. 1.

In general, vehicle 50 is controlled according to block 248 as afunction of ascertained locating 31 and/or of ascertained classification32 of at least one object 3. In particular, an actuating signal 249 a,249 b for at least one actuator 51 a, 51 b, 52 through 54 of vehicle 50is provided in subblock 249 as a function of locating 31 and/or ofclassification 32, and this actuator 51 a, 51 b, 52 through 54 iscontrolled via this actuating signal 249 a, 249 b in step(s) 250 and/or260.

FIG. 5 shows an example of a traffic situation in which method 100, 200may be applied. A vehicle 50 with radar sensor 1, which includes atransmitter 11 and a receiver 12, follows a trajectory 50 a andapproaches an intersection 60 on road 61, at which road 61 meets furtherroads 62 through 64. Transmitter 11 of radar sensor 1 emits radar waves13 into a monitored area 2. Radar waves 14 reflected on objects 3 inmonitored area 2 are recorded by receiver 12 of radar sensor 1. Forexample, metal trash bin 3′ reflects at position 3 b′ on the roadintersection.

In the example shown in FIG. 5, a further vehicle follows trajectory 3 band approaches intersection 60 on road 64. The vehicle at itsinstantaneous location 3 b is detected as an object 3 by radar sensor 1.Since trajectory 3 a of object 3 intersects trajectory 50 a of vehicle50, countermeasures are taken on board vehicle 50.

In the situation shown in FIG. 5, the benefit of method 100, 200 inparticular is that the locating and classification of vehicle 3 are lessadversely affected by metal trash bin 3′, which is situated in the samedirection relative to the radar sensor. Since trash bin 3′ is muchsmaller than vehicle 3, but on the other hand is also much closer toradar sensor 1, radar waves 14′ transmitted back from trash bin 3′ maygenerate a signal in receiver 12 that has a strength similar to radarwaves 14 transmitted back from vehicle 3. By use of piece ofdimensioning information 16, the signal contributions from trash bin 3′on the one hand and from vehicle 3 on the other hand may be reliablydistinguished from one another.

What is claimed is:
 1. A method for locating and/or classifying at leastone object in an area that is monitored by at least one radar sensor,the radar sensor including at least one transmitter and at least onereceiver for radar waves, the method comprising the following steps:converting a signal recorded by the receiver into a two- ormultidimensional frequency representation; supplying at least a portionof the two- or multidimensional frequency representation as an input toan artificial neural network (ANN) which includes a sequence of layerswith neurons, at least one of the layers of the ANN being additionallysupplied with a piece of dimensioning information which characterizes asize and/or absolute position of objects detected in the portion of thetwo- or multidimensional frequency representation; and taking a locationof the object and/or a classification of the object from the ANN as anoutput.
 2. The method as recited in claim 1, wherein the piece ofdimensioning information characterizes: a distance of at least onelocation, detected by the portion of the two- or multidimensionalfrequency representation, from the radar sensor, and/or an azimuth angleat which at least one location, detected by the portion of the two- ormultidimensional frequency representation, is situated relative to theradar sensor, and/or a distance of multiple locations, detected by theportion of the two- or multidimensional frequency representation, fromone another.
 3. The method as recited in claim 1, wherein the piece ofdimensioning information is supplied to the at least one of the layersof the ANN as a further input variable that is independent of a contentof the portion of the two- or multidimensional frequency representation.4. The method as recited in claim 1, wherein the piece of dimensioninginformation is added to the portion of the two- or multidimensionalfrequency representation as an additional information layer, and/or thepiece of dimensioning information is superimposed on the input of the atleast one of layers of the ANN.
 5. The method as recited in claim 1,wherein the piece of dimensioning information is taken from the two- ormultidimensional frequency representation.
 6. The method as recited inclaim 1, wherein the piece of dimensioning information is taken: (i)from measuring data that have been detected via a further sensor that isdifferent from the radar sensor, and/or (ii) from a digital map.
 7. Themethod as recited in claim 1, wherein in the portion of the two- ormultidimensional frequency representation, information outside a two- ormultidimensional sphere is suppressed around a center.
 8. A method forlocating and/or classifying at least one object in an area that ismonitored by at least one radar sensor, the radar sensor including atleast one transmitter and at least one receiver for radar waves, themethod comprising the following steps: converting a signal recorded bythe receiver at various points in time in each case into a two- ormultidimensional frequency representation; supplying at least a portionof each of the two- or multidimensional frequency representations as aninput to an artificial neural network (ANN) which includes a sequence oflayers with neurons; and taking a location of the object and/or aclassification of the object from the ANN as an output.
 9. The method asrecited in claim 8, wherein the ANN is a recurrent ANN in which anoutput of at least one neuron is supplied as an input to at least oneneuron of the same layer, or to at least one neuron of a preceding layerin the sequence.
 10. The method as recited in claim 8, wherein multipleportions of the two- or multidimensional frequency representations arejointly supplied to the same ANN as inputs.
 11. The method as recited inclaim 8, wherein multiple portions of the two- or multidimensionalfrequency representations are supplied in succession to the same ANN asinputs, and the locations and/or the classifications obtained in eachcase from the ANN are aggregated to form a location and/or aclassification.
 12. The method as recited in claim 10, wherein theclassifications are aggregated by a majority vote to form aclassification.
 13. The method as recited in claim 1, wherein the two-or multidimensional frequency representation is selected in which onedirection represents a distance of the object from the radar sensor, anda further direction represents an azimuth angle of the object relativeto the radar sensor.
 14. The method as recited in claim 1, wherein avehicle is controlled as a function of the ascertained location and/orof the ascertained classification of at least one object.
 15. The methodas recited in claim 1, wherein an actuating signal is provided for atleast one actuator of a vehicle as a function of the ascertainedlocation and/or of the ascertained classification, and the actuator iscontrolled with the actuating signal.
 16. The method as recited in claim1, wherein in response to an object having been recognized and anup-to-date planned and/or traveled trajectory of a vehicle contactingthe location and/or the trajectory of the object: (i) a warning deviceis activated, and/or (ii) a drive system and/or a braking system and/ora steering system of the vehicle is controlled to prevent the contact.17. A non-transitory machine-readable data medium on which is stored acomputer program for locating and/or classifying at least one object inan area that is monitored by at least one radar sensor, the radar sensorincluding at least one transmitter and at least one receiver for radarwaves, the computer program, when executed by a computer, causing thecomputer to perform the following steps: converting a signal recorded bythe receiver into a two- or multidimensional frequency representation;supplying at least a portion of the two- or multidimensional frequencyrepresentation as an input to an artificial neural network (ANN) whichincludes a sequence of layers with neurons, at least one of the layersof the ANN being additionally supplied with a piece of dimensioninginformation which characterizes a size and/or absolute position ofobjects detected in the portion of the two- or multidimensionalfrequency representation; and taking a location of the object and/or aclassification of the object from the ANN as an output.
 18. A computerand/or control unit and/or device configured to locate and/or classifyat least one object in an area that is monitored by at least one radarsensor, the radar sensor including at least one transmitter and at leastone receiver for radar waves, the computer and/or control unit and/ordevice configured to: convert a signal recorded by the receiver into atwo- or multidimensional frequency representation; supply at least aportion of the two- or multidimensional frequency representation as aninput to an artificial neural network (ANN) which includes a sequence oflayers with neurons, at least one of the layers of the ANN beingadditionally supplied with a piece of dimensioning information whichcharacterizes a size and/or absolute position of objects detected in theportion of the two- or multidimensional frequency representation; andtake a location of the object and/or a classification of the object fromthe ANN as an output.